Leveraging Large Models to Evaluate Novel Content: A Case Study on Advertisement Creativity
This work addresses the problem of automating creativity evaluation for advertisements, but it is incremental as it builds on existing marketing concepts and vision language models.
The authors tackled the challenge of evaluating visual advertisement creativity by breaking it into atypicality and originality, creating a benchmark with human annotations, and found that state-of-the-art vision language models show both promise and challenges in aligning with human assessments.
Evaluating creativity is challenging, even for humans, not only because of its subjectivity but also because it involves complex cognitive processes. Inspired by work in marketing, we attempt to break down visual advertisement creativity into atypicality and originality. With fine-grained human annotations on these dimensions, we propose a suite of tasks specifically for such a subjective problem. We also evaluate the alignment between state-of-the-art (SoTA) vision language models (VLMs) and humans on our proposed benchmark, demonstrating both the promises and challenges of using VLMs for automatic creativity assessment.